RT info:eu-repo/semantics/article
T1 Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea
A1 Álvarez, Daniel
A1 Crespo, Andrea
A1 Vaquerizo-Villar, Fernando
A1 Gutiérrez-Tobal, Gonzalo C
A1 Cerezo-Hernández, Ana
A1 Barroso-García, Verónica
A1 Ansermino, 
AD  J Mark
A1 Dumont, Guy A
A1 Hornero, Roberto
A1 del Campo, Félix
A1 Garde, Ainara
AB Objective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). Approach: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ⩾ 5 events h−1 from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. Main results: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p < 0.01) between children with AHI < 5 events h−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p <0.01) outperforming the other models. Significance: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.
PB IOP Publishing
SN 0967-3334
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/74132
UL https://uvadoc.uva.es/handle/10324/74132
LA eng
NO Physiological Measurement, 2018, vol. 39, p. 104002 (16pp)
NO Producción Científica
DS UVaDOC
RD 22-ene-2025